hardhat: Construct Modeling Packages

Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input.

Version: 1.4.0
Depends: R (≥ 3.5.0)
Imports: cli (≥ 3.6.0), glue (≥ 1.6.2), rlang (≥ 1.1.0), tibble (≥ 3.2.1), vctrs (≥ 0.6.0)
Suggests: covr, crayon, devtools, knitr, Matrix, modeldata (≥ 0.0.2), recipes (≥ 1.0.5), rmarkdown (≥ 2.3), roxygen2, testthat (≥ 3.0.0), usethis (≥ 2.1.5), withr (≥ 3.0.0)
Published: 2024-06-02
DOI: 10.32614/CRAN.package.hardhat
Author: Hannah Frick ORCID iD [aut, cre], Davis Vaughan [aut], Max Kuhn [aut], Posit Software, PBC [cph, fnd]
Maintainer: Hannah Frick <hannah at posit.co>
BugReports: https://github.com/tidymodels/hardhat/issues
License: MIT + file LICENSE
URL: https://github.com/tidymodels/hardhat, https://hardhat.tidymodels.org
NeedsCompilation: no
Materials: README NEWS
CRAN checks: hardhat results


Reference manual: hardhat.pdf
Vignettes: Forging data for predictions
Molding data for modeling
Creating Modeling Packages With hardhat


Package source: hardhat_1.4.0.tar.gz
Windows binaries: r-devel: hardhat_1.4.0.zip, r-release: hardhat_1.4.0.zip, r-oldrel: hardhat_1.4.0.zip
macOS binaries: r-release (arm64): hardhat_1.4.0.tgz, r-oldrel (arm64): hardhat_1.3.1.tgz, r-release (x86_64): hardhat_1.4.0.tgz, r-oldrel (x86_64): hardhat_1.3.1.tgz
Old sources: hardhat archive

Reverse dependencies:

Reverse imports: agua, applicable, baguette, brulee, card, censored, cuda.ml, dann, dials, healthyR.ts, modeltime, modeltime.resample, parsnip, probably, recipes, tabnet, themis, tidyclust, tidycmprsk, tidymodels, tune, vetiver, waywiser, workflows, workflowsets, yardstick
Reverse suggests: autostats, embed, healthyR.ai, mmrm, nestedmodels, textrecipes


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